Method and apparatus for optimizing synthetic conditions for generation of target products

ABSTRACT

A method of optimizing synthetic conditions includes receiving a graph-type descriptor comprising at least one of structural information of at least one reactant and structural information of a target product to be synthesized by the reactant; determining combinations of synthetic conditions for generating the target product by applying the graph-type descriptor to a prediction neural network model; selecting at least one initial condition combination from among the combinations based on a first confidence corresponding to a yield of the combinations; updating the prediction neural network model based on a ground-truth yield obtained from a result of an experiment with the initial condition combination; determining a priority of the combinations based on the updated prediction neural network model; and determining subsequent combinations of synthetic conditions based on the determined priority.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority from Korean Patent Application No.10-2021-0114066 filed on Aug. 27, 2021, and Korean Patent ApplicationNo. 10-2021-0155792 filed on Nov. 12, 2021, in the Korean IntellectualProperty Office, the disclosures of which are incorporated herein byreference in their entireties.

BACKGROUND 1. Field

Methods and apparatuses consistent with example embodiments relate to aoptimizing synthetic conditions for synthesizing chemical compounds togenerate target products.

2. Description of the Related Art

A neural network may refer to a computing architecture that models abiological brain. As the neural network advances, electronic devicesused in various fields may use a neural network-based model to analyzeinput data and extract and/or output valid information.

For example, synthesizing one or more compounds to execute chemicalreactions to obtain a target product may involve numerous experimentsperformed by a great number of human resources, and the stability ofsynthetic conditions obtained from the results of these experiments maynot be readily identifiable. Thus, there is a desire for a technologyfor reducing the number of times of synthesis experiments and obtaininga high yield of target products using a neural network-based model.

SUMMARY

One or more example embodiments may address at least the above problemsand/or disadvantages and other disadvantages not described above. Also,the example embodiments are not required to overcome the disadvantagesdescribed above, and an example embodiment may not overcome any of theproblems described above.

According to an aspect of various example embodiments, there is provideda method of optimizing synthetic conditions, the method includingreceiving a graph-type descriptor including at least one of structuralinformation of at least one reactant and structural information of atarget product to be synthesized by the reactant, determiningcombinations of synthetic conditions for generating the target productby applying the graph-type descriptor to a prediction neural networkmodel, selecting at least one initial condition combination from amongthe combinations based on a first confidence corresponding to a yield ofthe combinations, updating the prediction neural network model based ona ground-truth yield obtained from a result of an experiment with theinitial condition combination, determining a priority of thecombinations based on the updated prediction neural network model, anddetermining subsequent combinations of synthetic conditions based on thedetermined priority.

The selecting of the initial condition combination may includedetermining a selection ratio for the initial condition combinationbased on the first confidence and selecting the initial conditioncombination based on the selection ratio.

The determining of the selection ratio may include determining theselection ratio including a first ratio by which synthetic conditionspredicted by the prediction neural network model are selected to be theinitial condition combination and a second ratio by which randomconditions are selected to be the initial condition combination, basedon the first confidence.

The determining of the selection ratio may include increasing the firstratio and decreasing the second ratio when the first confidence isgreater than a preset threshold, and increasing the second ratio anddecreasing the first ratio when the first confidence is less than thepreset threshold.

The prediction neural network model may be trained to predict at leastone of the combinations, predicted yields corresponding to thecombinations, and the first confidence corresponding to the predictedyields.

The prediction neural network model may use a message passing neuralnetwork (MPNN) configured to determine the combinations according to acorrelation between neighboring atoms in the reactant based on thegraph-type descriptor.

The updating of the prediction neural network model may include updatingthe prediction neural network model based on a result of comparing apredicted yield corresponding to a combination of synthetic conditionsdetermined by the prediction neural network model and the ground-truthyield.

The determining of the priority may include comparing a first yieldcorresponding to a combination of synthetic conditions predicted by theprediction neural network model yet to be updated and a second yieldcorresponding to a combination of synthetic conditions predicted by theupdated prediction neural network model, and determining a prioritybetween the combinations based on a result of the comparing.

The determining of the priority between the combinations based on theresult of the comparing may include determining a first combinationcorresponding to the first yield to have a higher priority anddetermining a second combination corresponding to the second yield tohave a lower priority when the second yield is less than or equal to thefirst yield, and determining the second combination to have the higherpriority and the first combination to have the lower priority when thesecond yield is greater than the first yield.

The determining of the subsequent combinations of the syntheticconditions may include redetermining the priority as the predictionneural network model is updated based on a ground-truth yield obtainedfrom a result of an experiment with the subsequent combinations of thesynthetic conditions, determining next subsequent combinations ofsynthetic conditions that follow the subsequent combinations based onthe redetermined priority, and iteratively performing the redeterminingof the priority and the determining of the next subsequent combinationsuntil a ground-truth yield obtained from a result of an experiment withthe next subsequent combinations satisfies a preset target yield.

The synthetic conditions may include at least one of a catalystcondition, a ligand condition, a base condition, a solvent condition, atemperature condition, a density condition, a humidity condition, areaction time condition, and a pressure condition.

The graph-type descriptor further may include a synthetic path includingat least one of substituents and reactors of the reactant.

The determining of the combinations of the synthetic conditions mayinclude determining the combinations of the synthetic conditions tosatisfy a reaction mechanism of the reactant by applying the graph-typedescriptor to the prediction neural network model.

According to another aspect of various example embodiments, there isprovided a method of optimizing synthetic conditions, the methodincluding receiving a graph-type descriptor including structuralinformation of at least one reactant and structural information of atarget product to be synthesized by the reactant, and a synthetic pathof the reactant, and receiving a reaction mechanism of the reactant,determining a search space including combinations of syntheticconditions for generating the target product and the synthetic path thatsatisfy the reaction mechanism by applying the graph-type descriptor toa prediction neural network model, sampling at least one initialcondition combination among the combinations based on a first confidencecorresponding to a yield of the combinations in the search space,updating the prediction neural network model based on a ground-truthyield obtained from a result of an experiment with the initial conditioncombination, determining a priority of the combinations based on theupdated prediction neural network model, and redetermining the searchspace such that it includes subsequent combinations of syntheticconditions and a subsequent synthetic path based on the determinedpriority. As the prediction neural network model is updated based on aresult of an experiment with the subsequent combinations, the prioritymay be redetermined, and the search space may be determined to includenext subsequent combinations of synthetic conditions that follow thesubsequent combinations based on the redetermined priority.

The sampling of the initial condition combination may includedetermining a selection ratio including a first ratio by which syntheticconditions predicted by the prediction neural network model are selectedto be the initial condition combination and a second ratio by whichrandom conditions are selected to be the initial condition combination,based on the first confidence.

The determining of the selection ratio may include increasing the firstratio and decreasing the second ratio when the first confidence isgreater than a preset threshold, and increasing the second ratio anddecreasing the first ratio when the first confidence is less than thepreset threshold.

The prediction neural network model may be trained to predict at leastone of the combinations, predicted yields corresponding to thecombinations, the first confidence corresponding to the predictedyields, and the synthetic path, which satisfy the reaction mechanism.

The updating of the prediction neural network model may include updatingthe prediction neural network model based on a result of comparing apredicted yield corresponding to a combination of synthetic conditionsdetermined by the prediction neural network model and the ground-truthyield obtained from the result of the experiment with the initialcondition combination.

The determining of the priority may include comparing a first yieldcorresponding to a combination of synthetic conditions predicted by theprediction neural network model yet to be updated and a second yieldcorresponding to a combination of synthetic conditions predicted by theupdated prediction neural network model, determining a first combinationcorresponding to the first yield to have a higher priority anddetermining a second combination corresponding to the second yield tohave a lower priority when the second yield is less than or equal to thefirst yield, and determining the second combination to have the higherpriority and the first combination to be the lower priority when thesecond yield is greater than the first yield.

According to still another aspect of various example embodiments, thereis provided an apparatus for optimizing synthetic conditions, theapparatus including a user interface (UI) configured to receive agraph-type descriptor including at least one of structural informationof at least one reactant and structural information of a target productto be synthesized by the reactant, a memory in which at least oneprogram is stored, and a processor configured to operate a neuralnetwork by executing the program. The processor may determinecombinations of synthetic conditions for generating the target productby applying the graph-type descriptor to a prediction neural networkmodel based on the neural network, select at least one initial conditioncombination from among the combinations based on a first confidencecorresponding to a yield of the combinations, update the predictionneural network model based on a ground-truth yield obtained from aresult of an experiment with the initial condition combination,determine a priority of the combinations based on the updated predictedmodel, and determine subsequent combinations of synthetic conditionsbased on the determined priority. The processor may determine aselection ratio for the initial condition combination based on the firstconfidence, and select the initial condition combination based on thedetermined selection ratio.

The processor may determine the selection ratio including a first ratioby which synthetic conditions predicted by the prediction neural networkmodel are selected to be the initial condition combination and a secondratio by which random conditions are selected to be the initialcondition combination, based on the first confidence.

The processor may increase the first ratio and decrease the second ratiowhen the first confidence is greater than a preset threshold, andincrease the second ratio and decrease the first ratio when the firstconfidence is less than the preset threshold.

The prediction neural network model may be trained to predict at leastone of the combinations, predicted yields corresponding to thecombinations, and the first confidence corresponding to the predictedyields.

The prediction neural network model may use an MPNN configured todetermine the combinations according to a correlation betweenneighboring atoms in the reactant based on the graph-type descriptor.

The processor may update the prediction neural network model based on aresult of comparing a predicted yield of a combination of syntheticconditions determined by the prediction neural network model and theground-truth yield.

The processor may compare a first yield corresponding to a combinationof synthetic conditions predicted by the prediction neural network modelyet to be updated and a second yield corresponding to a combination ofsynthetic conditions predicted by the updated prediction neural networkmodel, and determine a priority between the combinations based on aresult of the comparing.

The processor may determine a first combination corresponding to thefirst yield to have a higher priority and determine a second combinationcorresponding to the second yield to have a lower priority when thesecond yield is less than or equal to the first yield, and determine thesecond combination to have the upper priority and the first combinationto be the lower priority when the second yield is greater than the firstyield.

The processor may redetermine the priority as the prediction neuralnetwork model is updated based on a ground-truth yield obtained from aresult of an experiment with the subsequent combinations of thesynthetic conditions, determine next subsequent combinations ofsynthetic conditions based on the redetermined priority, and iterativelyperform the redetermining of the priority and the determining of thenext subsequent combinations until a ground-truth yield obtained from aresult of an experiment with the next subsequent combinations satisfiesa preset target yield.

The synthetic conditions may include at least one of a catalystcondition, a ligand condition, a base condition, a solvent condition, atemperature condition, a density condition, a humidity condition, areaction time condition, and a pressure condition.

The graph-type descriptor may further include a reaction mechanism ofthe reactant.

The processor may determine the combinations of the synthetic conditionsto satisfy a reaction mechanism of the reactant by applying thegraph-type descriptor to the prediction neural network model.

According to yet another aspect of various example embodiments, there isprovided an apparatus for optimizing synthetic conditions, the apparatusincluding a UI configured to receive a graph-type descriptor includingstructural information of at least one reactant, structural informationof a target product to be synthesized by the reactant, and a syntheticpath including at least one of substituents and reactors of thereactant, and receive a reaction mechanism of the reactant, a memory inwhich at least one program is stored, and a processor configured tooperate a neural network by executing the program. The processor maydetermine a search space including combinations of synthetic conditionsfor generating the target product and a synthetic path, which satisfythe reaction mechanism, by applying the graph-type descriptor to aprediction neural network model based on the neural network, sample atleast one initial condition combination among the combinations based ona first confidence corresponding to a yield of the combinations in thesearch space, update the prediction neural network model based on aground-truth yield obtained from a result of an experiment with theinitial condition combination, determine a priority of the combinationsbased on the updated prediction neural network model, and redeterminethe search space such that it includes subsequent combinations ofsynthetic conditions based on the determined priority. Here, as theprediction neural network model is updated based on a ground-truth yieldobtained from a result of an experiment with the subsequent combinationsof the synthetic conditions, the priority may be redetermined, and thesearch space may be determined such that it includes next subsequentcombinations of synthetic conditions based on the redetermined priority.

The prediction neural network model may be trained to predict at leastone of the combinations, predicted yields corresponding to thecombinations, the first confidence corresponding to the predictedyields, and the synthetic path, which satisfy the reaction mechanism.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or other aspects will be more apparent by describingcertain example embodiments, with reference to the accompanyingdrawings, in which:

FIG. 1 is a diagram illustrating an example of a reactant, a targetproduct, a synthetic path, synthetic conditions, and a reactionmechanism according to an example embodiment;

FIG. 2 is a diagram illustrating a conceptual example of a method ofoptimizing synthetic conditions according to an example embodiment;

FIG. 3 is a flowchart illustrating an example of a method of optimizingsynthetic conditions according to an example embodiment;

FIG. 4 is a diagram illustrating an example of determining combinationsof synthetic conditions according to an example embodiment;

FIG. 5 is a diagram illustrating an example of selecting an initialcondition combination according to an example embodiment;

FIG. 6 is a flowchart illustrating an example of determining a priorityof combinations according to an example embodiment;

FIG. 7 is a flowchart illustrating an example of a method of determiningsubsequent combinations of synthetic conditions according to an exampleembodiment;

FIG. 8 is a flowchart illustrating another example of a method ofoptimizing synthetic conditions according to an example embodiment;

FIG. 9 is a flowchart illustrating still another example of optimizingsynthetic conditions according to an example embodiment;

FIG. 10 is a diagram illustrating another example of a method ofoptimizing synthetic conditions according to an example embodiment; and

FIG. 11 is a diagram illustrating an example of an apparatus foroptimizing synthetic conditions according to an example embodiment.

DETAILED DESCRIPTION

Example embodiments are described in greater detail below with referenceto the accompanying drawings.

In the following description, like drawing reference numerals are usedfor like elements, even in different drawings. The matters defined inthe description, such as detailed construction and elements, areprovided to assist in a comprehensive understanding of the exampleembodiments. However, it is apparent that the example embodiments can bepracticed without those specifically defined matters. Also, well-knownfunctions or constructions are not described in detail since they wouldobscure the description with unnecessary detail.

Although terms of “first” or “second” are used to explain variouscomponents, the components are not limited to the terms. These termsshould be used only to distinguish one component from another component.For example, a “first” component may be referred to as a “second”component, or similarly, and the “second” component may be referred toas the “first” component within the scope of the right according to theconcept of the present disclosure.

It will be understood that when a component is referred to as being“connected to” another component, the component can be directlyconnected or coupled to the other component or intervening componentsmay be present.

As used herein, the singular forms are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It shouldbe further understood that the terms “comprises,” “comprising,”“includes,” and/or “including,” when used in this specification, specifythe presence of stated features, integers, steps, operations, elements,components or a combination thereof, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. Expressions such as “atleast one of,” when preceding a list of elements, modify the entire listof elements and do not modify the individual elements of the list. Forexample, the expression, “at least one of a, b, and c,” should beunderstood as including only a, only b, only c, both a and b, both a andc, both b and c, or all of a, b, and c.

Unless otherwise defined herein, all terms used herein includingtechnical or scientific terms have the same meanings as those generallyunderstood by one of ordinary skill in the art. Terms defined indictionaries generally used should be construed to have meaningsmatching with contextual meanings in the related art and are not to beconstrued as an ideal or excessively formal meaning unless otherwisedefined herein.

FIG. 1 is a diagram illustrating an example of a reactant, a targetproduct, synthetic paths, synthetic conditions, and a reaction mechanismaccording to an example embodiment. Referring to FIG. 1 , reactants 110and 120, a target product 125, synthetic paths 130 and 140, syntheticconditions 150, 160, and 170, and a reaction mechanism are illustrated.

The term “reactant(s)” refers to a material or substance participatingin a chemical reaction, and the term “product(s)” refers to a materialor substance generated as the result of the chemical reaction orchemical synthesis. That is, a material or substance reacting when itchanges into another material or substance through a chemical reactionmay be referred to as a reactant(s), and a material or substance changedor newly generated through the chemical reaction may be referred to as aproduct(s).

The term “target product” may be a product targeted to be generatedthrough a chemical reaction of a reactant(s). The target product mayalso be referred to as a target compound.

For example, as illustrated, the target product 125 may be generatedthrough a synthesis of the reactants 110 and 120. In this example, thetarget product 125 may be a known or unknown material or substance.

For a synthesis of reactants to generate a target product, conditionsincluding, for example, a catalyst condition (e.g., a catalyst of 6.25mol % Pd(OAc)₂), a ligand condition (e.g., 12 ligands of 12.5 mol %),and a base condition (e.g., 2.5 equivalents of 8 bases), and also atemperature condition (e.g., 100° C.), a reaction time condition (e.g.,1 minute (min)), and a pressure condition (e.g., a flow rate of 1 mL/minat 100 bar), may be applied.

For example, for the synthesis of the reactants 110 and 120, varioussynthetic conditions such as a catalyst condition, a ligand condition150, a base condition 160, a solvent condition 170, a temperaturecondition, a density condition, a humidity condition, a reaction timecondition, a pressure condition, and the like may be applied, butexamples of which are not limited thereto. Among these syntheticconditions, the catalyst condition, the ligand condition 150, the basecondition 160, and the solvent condition 170 may be collectivelyreferred to as a reagent condition.

A product may change according to synthetic conditions applied to thesynthesis of the reactants 110 and 120. In other words, if the syntheticconditions change, the resulting product may change. A ligand usedherein may correspond to a specific substitute or specific moiety in amolecule responsible for a characteristic chemical reaction of moleculesin organic chemistry. The ligand may be an atom or a group of atoms thatforms a coordinate bond while donating an electron pair to a centralmetal atom in a substance or complex compound that specifically binds toa specific site (or a ligand binding site) in a large molecule such as areceptor, for example. The ligand may also be referred to as afunctional group in that it is a group of atoms with characteristicchemical behavior.

The synthetic conditions may be various conditions set to perform anexperiment for generating the target product 125 using the reactants 110and 120. The synthetic conditions may be determined in various waysaccording to a reaction mechanism.

The reaction mechanism used herein may refer to a chemical reactionmethod for generating a target product using a reactant(s) to besynthesized. The reaction mechanism may include, for example, aSuzuki-Miyaura reaction, a Buchwald-Hartwig reaction, and an arylationreaction, but is not limited thereto. For example, generating a targetproduct, for example, halide (R2-BY2), using a reactant, for example,organoboron (R1-BY2), may involve a reaction mechanism, for example, theSuzuki-Miyaura reaction. The reaction mechanism may be provided as aplurality of reaction mechanisms based on structural information of areactant(s) and structural information of a target product, for example,A molecular structure+B molecular structure=C molecular structure. Astructure used herein may refer to an atomic-level structure of asubstance or material. The structure may indicate a structural formulabased on a bond between atoms.

In addition to the synthetic conditions described above, varioussynthetic paths (e.g., synthetic paths 130 and 140) may be suggested.The synthetic paths may be another target to be optimized and may beoptimized along with the synthetic conditions.

The synthetic paths may include, for example, at least one ofsubstitutes 130 and reactors 140 of the reactants 110 and 120.

A substitute(s) used herein may correspond to, for example, an atom or agroup of atoms substituting one or more hydrogen atoms (R1) on a parentchain of hydrocarbon in organic chemistry and/or biochemistry. Forexample, when one hydrogen (H) atom in benzene (C₆H₆) is substitutedwith a chlorine (Cl) atom, it may become chlorobenzene (C₆H₅C₁). Whenone hydrogen (H) atom of benzene (C₆H₆) is substituted with a nitrogroup (NO₂), it may become nitrobenzene (C₆H₅NO₂). In this example, thechlorine atom and the nitro group substituted with one hydrogen atom maycorrespond to substituents.

A reactor(s) used herein may correspond to a starting material orsubstance for generating a target product.

In general, a great amount of cost for reagents, a great amount of timefor experiments, and a great number of human resources may be requiredto obtain a target product.

In an example, optimal combinations of synthetic conditions forsynthesizing a target product with a high yield may be obtained using aneural network-based prediction model, the reactors 140 may bepredicted, and/or a search space including the synthetic conditions 150,160, and 170, and the synthetic paths 130 and 140 may be determined. Theyield of the chemical synthesis process may be represented by a reactionyield indicating the quantity of the target product formed in relationto the reactant consumed, a conversion rate indicating an amount of areactant that has reacted in relation to the total reactant, and/or aselectivity rate indicating a ratio of the quantity of the (desired)target product to the amount of an undesired target product that isformed as a result of the chemical synthesis process.

In addition, a synthetic path(s) may be classified as a target for asynthetic condition, and various synthetic paths may be searched inconjunction with the prediction model. In an example, the predictionmodel may be updated based on a difference between an actual yield(e.g., a ground-truth yield) measured from a result of an experimentperformed with an initial condition combination and a predicted yield ofthe prediction model, and a priority among the combinations of thesynthetic conditions may be determined. Based on the determinedpriority, a search space including subsequent combinations of syntheticconditions and synthetic paths for a subsequent experiment may beiteratively redetermined. Thus, a combination of synthetic conditionsand a synthetic path from deriving an optimal yield may be obtained.

FIG. 2 is a diagram illustrating a conceptual example of a method ofoptimizing synthetic conditions according to an example embodiment.Referring to FIG. 2 , an apparatus for optimizing synthetic conditions(hereinafter simply an “optimizing apparatus”) may optimize syntheticconditions.

In the related art, a fixed number of initial experiments (e.g., initial10 experiments) may be conducted by randomly combining conditions or byhuman knowledge, in order to find an optimal combination of conditions.According to an embodiment of the disclosure, the initial experiment maybe conducted using a certain number of condition combinationsrecommended by the prediction model, and the number of trials of thecondition combinations predicted by machine learning may be adjustedaccording to the reliability of the predictive model. For example, whenthe reliability of the predictive model is high, all preset number ofexperiments (e.g., 10 experiments) may be conducted with confidence inthe results of the predictive model. When the reliability of thepredictive model is low, the number of experiments may be adjusted to belower such that the optimal combination of conditions can be found morequickly.

In operation 210, the optimizing apparatus may receive a synthetictarget to be synthesized. The synthetic target may be, for example, animage, a graph-type descriptor including three-dimensional (3D)structural information of at least one reactant and 3D structuralinformation of a target product to be synthesized by the reactant, or agraph-type descriptor including 3D structural information of a targetproduct. However, examples of the synthetic target are not limited tothe foregoing example. The synthetic target may further include asynthetic path or may correspond to a search space including acombination of synthetic conditions and a synthetic path.

In operation 220, the optimizing apparatus may perform an initialexperiment by selecting an initial condition combination 225 based oncombinations of synthetic conditions predicted by a prediction (neuralnetwork) model trained in advance for the synthetic target received inoperation 210. For example, the optimizing apparatus may perform theinitial experiment by selecting a plurality of initial conditioncombinations (e.g., 10 initial condition combinations) to be used forthe initial experiment based on the combinations of the syntheticconditions 222 predicted by the prediction model (which followssynthesis conditions 221 found through exploration), yields respectivelycorresponding to the combinations, and a confidence corresponding to theyields. An initial condition combination used herein may correspond to agroup of combinations of synthetic conditions to be applied to aninitial experiment for synthesizing a target product, or a group of thesynthetic conditions.

The prediction model may be trained through data-based machine learningthat stores and/or processes one million or more optimized conditionscited in, for example, scientific papers and patents, in the form of adatabase. The prediction model may output the combinations of thesynthetic conditions for the target product by receiving the descriptorin operation 210. An example of the prediction model will be describedin detail with reference to FIG. 4 .

The prediction model may predict a score corresponding to data (e.g.,combinations of synthetic conditions) used for training, and theoptimizing apparatus may perform the initial experiment using 10 sets ofdata with highest scores among sets of data, that is, sets of data withhighest yields.

The prediction model may predict combinations of synthetic conditions.However, for relatively uncommon experimental data, the prediction modelmay predict an uncertain combination of synthetic conditions. Thus, theoptimizing apparatus may output both a predicted result (e.g.,combinations of synthetic conditions and yields corresponding to thecombinations of the synthetic conditions) of the prediction model and aconfidence (e.g., a first confidence) corresponding to the predictedresult, and use the predicted result by a predetermined ratio based onthe confidence.

For example, under the assumption that combinations of 10 syntheticconditions are used for the initial experiment, the optimizing apparatusmay select at least one initial condition combination to be used for theexperiment from among 10 sets of data based on the predicted result andthe confidence. In this example, when the first confidence correspondingto the predicted result of the prediction model is high (or higher thana preset confidence level), selecting combinations of syntheticconditions predicted by the prediction model for all ten times may beeffective in terms of optimization efficiency. In contrast, when thefirst confidence corresponding to the predicted result of the predictionmodel is low (or lower than or equal to the preset confidence level),performing the experiment with the synthetic conditions predicted by theprediction model for all ten times may not be desirable, and thus usingdifferent synthetic conditions for some of the ten times may bedesirable.

The first confidence may also be referred to as an uncertainty of theprediction model in that it indicates whether a predicted result of theprediction model is certain or uncertain. Hereinafter, the firstconfidence, the uncertainty, and a confidence corresponding to a yieldwill be interchangeably used.

In operation 220, the optimizing apparatus may select a combination ofsynthetic conditions predicted by the prediction model by a first ratio(e.g., a mixing ratio of chemical components) from among 10 syntheticconditions to be used for the initial experiment, or select a newcombination of synthetic conditions by a second ratio (e.g., anothermixing ratio of chemical components), based on the first confidence. Forexample, when a predicted result of the prediction model is uncertain,that is, when the first confidence is determined to be less than apreset threshold, the optimizing apparatus may decrease the first ratioof the combination of the synthetic conditions predicted by theprediction model among the combinations of the 10 synthetic conditionsto be used in the initial experiment, and increase the second ratio ofthe new combination of the synthetic conditions different from thecombination of the synthetic conditions predicted by the predictionmodel among the combinations of the 10 synthetic conditions. Conversely,when the predicted result of the prediction model is certain, that is,the first confidence is determined to be greater than the presetthreshold, the optimizing apparatus may increase the first ratio of thecombination of the synthetic conditions predicted by the predictionmodel among the combinations of the 10 synthetic conditions, anddecrease the second ratio of the new combination of the syntheticconditions among the combinations of the 10 synthetic conditions.

In operation 230, the optimizing apparatus may obtain an actual yieldfrom a result of the experiment performed with the 10 initial conditioncombinations selected in operation 220.

In operation 240, the optimizing apparatus may calculate a priorityamong combinations of synthetic conditions for a subsequent experiment,i.e., subsequent combinations of synthetic conditions, based on theactual yield obtained in operation 230. The optimizing apparatus mayoptimize the combinations of the synthetic conditions for the subsequentexperiment by verifying a difference between the actual yield obtainedthrough the experiment and a predicted yield of the prediction model.

In operation 240, the optimizing apparatus may rapidly find an optimalcombination of synthetic conditions with a high yield, using both acombination of synthetic conditions obtained by the prediction modelusing known data and a combination of synthetic conditions obtainedthrough Bayesian optimization using an actual experiment result.

The optimizing apparatus may determine the priority among the subsequentcombinations of the synthetic conditions (or the combinations of thesynthetic conditions for the subsequent experiment) as illustrated in agraph 250 through a comprehensive determination made based on a yield(indicated as a graph neural network (GNN)) predicted by the predictionmodel trained with data, an uncertainty (u) of the prediction model, ayield (indicated as BO) calculated through a yield inference method suchas Bayesian optimization that is based on an actual experiment result,and the number of iterations (T) as illustrated in a graph 245. In thiscase, the prediction model may be updated based on the actual yield fromthe result of the experiment performed with the subsequent combinationsof the synthetic conditions and the priority may thereby beredetermined, and then next subsequent combinations of syntheticconditions that follow the subsequent combinations may be determinedbased on the redetermined priority and the priority among the nextsubsequent combinations may be determined. The priority may beiteratively determined in such a way.

When an actual yield obtained according to the redetermined prioritydoes not reach a target yield, the optimizing apparatus may iterativelyperform operations 230 and 240 by feeding the actual yield back. Whenthe actual yield obtained according to the redetermined priority reachesor exceeds the target yield, the optimizing apparatus may end theoperations.

FIG. 3 is a flowchart illustrating an example of a method of optimizingsynthetic conditions according to an example embodiment. Operations tobe described hereinafter may be performed in sequential order, but notbe necessarily performed in sequential order. For example, the order ofthe operations may change, and at least two of the operations may beperformed in parallel.

Referring to FIG. 3 , the optimizing apparatus may perform operations310 through 360 to optimize synthetic conditions for generating a targetproduct.

According to an example embodiment, the optimizing apparatus may beimplemented as one of the various types of apparatuses or devices, forexample, a personal computer (PC), a server device, a mobile device, anembedded device, and the like. In more detail, the optimizing apparatusmay be, as a non-limiting example, a smartphone, a tablet device, anaugmented reality (AR) device, an Internet of things (IoT) device, anautonomous vehicle, a robot, a medical device, or the like, whichperforms speech recognition, image recognition, image classification,and the like using a neural network. Alternatively, the optimizingapparatus may correspond to a dedicated hardware (HW) acceleratorprovided to the foregoing apparatuses or devices, or to an HWaccelerator such as a neural processing unit (NPU), a tensor processingunit (TPU), and a neural engine which are a dedicated module foroperating a neural network. However, examples of the optimizingapparatus are not limited to the foregoing.

In operation 310, the optimizing apparatus may receive a graph-typedescriptor including at least one of structural information of at leastone reactant and structural information of a target product to besynthesized by the reactant. The structural information of the reactantand the structural information of the target product may correspond to3D structural information. Structural information used herein, anindicator used to represent a structure of a substance or material, mayrepresent a structural feature value indicating whether a structure of aspecific part is included. The graph-type descriptor may further includea synthetic path including at least one of substituents and reactors ofthe reactant.

The optimizing apparatus may receive at least one of the structuralinformation of the reactant and the structural information of the targetproduct through a user interface (UI) (e.g., a UI 1110 of FIG. 11 ).

That is, the optimizing apparatus may receive the structural informationof the reactant and the structural information of the target product inthe form of a graph-type descriptor. A descriptor used herein maycorrespond to an indicator value used to represent features of amaterial or substance such as a reactant and/or a product.

According to an example embodiment, the optimizing apparatus may alsoreceive a reaction mechanism for generating a target product through areactant(s). The optimizing apparatus may receive the reaction mechanismin the form of a one-hot vector. For example, when the optimizingapparatus receives a second reaction mechanism among four reactionmechanisms, a one-hot vector received by the optimizing apparatus may berepresented as 0100.

In operation 320, the optimizing apparatus may determine combinations ofsynthetic conditions for generating the target product by applying thedescriptor to a neural network-based prediction model. A combination(s)of synthetic conditions used herein may be used to obtain an optimalyield and may correspond to a combination of conditions included in eachsynthetic condition. According to an example embodiment, the optimizingapparatus may generate the combinations of the synthetic conditionsbased on the structural information of the reactant and the structuralinformation of the target product. The prediction model may output thecombinations of the synthetic conditions for generating the targetproduct by receiving the descriptor. In this case, a neural network mayinclude a message-passing neural network (MPNN) that determinescombinations of synthetic conditions based on a correlation betweenneighboring atoms in a reactant(s) based on a graph-type descriptorindicating a relationship including nodes and edges. The MPNN may be aneural network trained with a message-passing algorithm and a collectingprocedure for calculating a function of an entire input graph amonggraph neural networks (GNNs) that are based on structural data ofgraphs.

In addition, the prediction model may use or store therein a predictedyield that is based on the structural information of the reactant, thestructural information of the target product, the reaction mechanism,and the synthetic conditions, and a confidence corresponding to thepredicted yield. The optimizing apparatus may adjust the predictionmodel by receiving the predicted yield and the confidence (e.g., a firstconfidence) corresponding to the predicted yield from the predictionmodel or receiving a user input as feedback. The prediction model mayuse or store therein the structural information of the reactant, thestructural information of the target product, and the reactionmechanism.

According to another example embodiment, when receiving the reactionmechanism along with the structural information of the reactant, thestructural information of the target product, the optimizing apparatusmay generate combinations of synthetic conditions that satisfy thereaction mechanism. For example, when the received reaction mechanismcorresponds to a Suzuki-Miyaura reaction, the optimizing apparatus mayselect a combination of synthetic conditions including a catalystcondition (e.g., a chemical species of nucleophile such as palladium)and a reaction time condition (e.g., 15 minutes (min)).

According to an example embodiment, the optimizing apparatus may setcombinations of synthetic conditions such that a type of syntheticcondition is common for the combinations of the synthetic conditions.For example, the optimizing apparatus may set a first combination ofsynthetic conditions to include a catalyst A and a reaction time of 10minutes, and set a second combination of synthetic conditions to includea catalyst B and a reaction time of 20 minutes. In this example, theoptimizing apparatus may set the combinations of the syntheticconditions such that the first combination of the synthetic conditionsand the second combination of the synthetic conditions include thecatalyst condition and the reaction time condition.

According to another example embodiment, the optimizing apparatus mayset combinations of synthetic conditions such that at least one of typesof synthetic conditions is different for the combinations of thesynthetic conditions. For example, the optimizing apparatus may set afirst combination of synthetic conditions to include a catalyst C and areaction time of 30 minutes, and set a second combination of syntheticconditions to include a base D and a temperature of E° C. In thisexample, the optimizing apparatus may set the first combination of thesynthetic conditions to include the catalyst condition and the reactiontime condition, and set the second combination of the syntheticconditions to include the base condition and the temperature conditionthat are different from the types of the synthetic conditions includedin the first combination of the synthetic conditions.

Alternatively, the optimizing apparatus may generate a syntheticcondition group including combinations of synthetic conditions. Thesynthetic condition group may refer to a group of combinations ofsynthetic conditions for which an experiment priority needs to bedetermined. The optimizing apparatus may determine the experimentpriority among the combinations of the synthetic conditions included inthe synthetic condition group.

In operation 320, the optimizing apparatus may calculate a predictedyield of the target product corresponding to each of the combinations ofthe synthetic conditions and a first confidence corresponding to thepredicted yield, using the neural network-based prediction model. Thepredicted yield used herein may refer to an expected value of a yield ofa target product generated from a reactant(s) through an experimentperformed on the reactant(s) using a reaction mechanism and a syntheticcondition. The predicted yield may be calculated by the predictionmodel. The predicted yield may be different from an actual yieldcalculated from an actual experiment.

The prediction model may include a neural network trained to predict atleast one of combinations of synthetic conditions, predicted yieldscorresponding to the combinations of the synthetic conditions, and afirst confidence corresponding to the predicted yields. The firstconfidence may refer to an indicator indicating a degree of similarityof a predicted yield of a target product generated by a combination ofsynthetic conditions to an actual yield, and may also be referred to asprediction accuracy. The prediction model may store therein at least onesynthetic condition corresponding to a reaction mechanism.

The prediction model may predict combinations of synthetic conditions.However, for relatively uncommon experimental data, the prediction modelmay predict an uncertain combination of synthetic conditions and maythus output a predicted result of the prediction model along with acorresponding confidence. Hereinafter, determining combinations ofsynthetic conditions by the optimizing apparatus will be described inmore detail with reference to FIG. 4 .

In operation 330, the optimizing apparatus may select at least oneinitial condition combination from among the combinations of thesynthetic conditions based on the first confidence corresponding to theyield of the combinations determined in operation 320. The initialcondition combination may be, for example, combinations of 10 to 20synthetic conditions used for an initial experiment, but is not limitedthereto.

The optimizing apparatus may determine a selection ratio for the initialcondition combination based on the first confidence. The selection ratiomay include a first ratio by which synthetic conditions predicted by theprediction model are selected to be the initial condition combination,and a second ratio by which random conditions are selected to be theinitial condition combination, but examples of which are not limitedthereto. The first ratio may also be referred to as an exploitationratio in that synthetic conditions are selected based on optimalconditions known or cited by, for example, (scientific) papers andpatents. The second ratio may also be referred to as an explorationratio in that synthetic conditions are selected from among unknownrandom conditions or conditions different from the synthetic conditionscorresponding to the first ratio.

When the first confidence is greater than a preset threshold, theoptimizing apparatus may increase the first ratio and decrease thesecond ratio. When the first confidence is less than the presetthreshold due to, for example, a lack of data and a new synthetic type,the optimizing apparatus may increase the second ratio and decrease thefirst ratio. Hereinafter, selecting an initial condition combination bythe optimizing apparatus will be described in more detail with referenceto FIG. 5 .

In operation 340, the optimizing apparatus may update the predictionmodel based on an actual yield measured from a result of an experimentwith the initial condition combination selected in operation 330. Theoptimizing apparatus may update the prediction model while optimizingthe synthetic conditions by adjusting the selection ratio based on aresult of comparing a predicted yield corresponding to a combination ofsynthetic conditions determined by the prediction model and the actualyield. When the predicted yield is greater than the actual yield, theoptimizing apparatus may update the prediction model such that thecombination of the synthetic conditions corresponding to the predictedyield is included by a greater proportion. In contrast, when thepredicted yield is less than the actual yield, the optimizing apparatusmay update the prediction module such that the combination of thesynthetic conditions corresponding to the actual yield is included by agreater proportion.

In operation 350, the optimizing apparatus may determine a priorityamong the combinations of the synthetic conditions based on theprediction model updated in operation 340. The optimizing apparatus maydetermine a priority among subsequent combinations of syntheticconditions, or combinations of synthetic conditions for a subsequentexperiment, based on a result of comparing a first yield correspondingto a combination of synthetic conditions predicted by the predictionmodel yet to be updated and a second yield corresponding to acombination of synthetic conditions predicted by the prediction modelupdated in operation 340.

The optimizing apparatus may calculate the priority (e.g., f_(UHDO))among the subsequent combinations of the synthetic conditions usingEquation 1, for example.

$\begin{matrix}{f_{UHDO} = {\frac{GNN}{uncertainty} + {{{BO} \cdot \log}T}}} & \left\lbrack {{Equation}1} \right\rbrack\end{matrix}$

In Equation 1, the parameter “GNN” denotes a predicted yieldcorresponding to a combination of synthetic conditions predicted by aGNN-based prediction model, and the parameter “uncertainty (u)” denotesan uncertainty of the prediction model. The uncertainty of theprediction model may be a confidence associated with the predictionmodel itself and may also be referred to as a second confidence.

The parameter “BO” denotes an actual yield corresponding to a syntheticcondition optimized through Bayesian optimization formatted to a resultof an actual experiment. The parameter “T” denotes the number ofiterations.

When the prediction model is determined to be less confident, theoptimizing apparatus may optimize combinations of synthetic conditionsby assigning a higher weight to a Bayesian optimization result based onthe actual experiment than a predicted result of the prediction model.For example, when a yield predicted by the prediction model is 30% and atarget yield is 95%, the optimizing apparatus may determine theprediction model to be less confident. In this example, the optimizingapparatus may assign a higher weight to a combination of syntheticconditions formatted to a result of a current experiment by Bayesianoptimization and may thus more rapidly find optimal combinations ofsynthetic conditions. Hereinafter, determining a priority by theoptimizing apparatus will be described in more detail with reference toFIG. 6 .

In operation 360, the optimizing apparatus may determine subsequentcombinations of synthetic conditions based on the priority determined inoperation 350.

In operation 360, the optimizing apparatus may redetermine the priorityas the prediction model is updated based on an actual yield from aresult of an experiment with the subsequent combinations of thesynthetic conditions. The optimizing apparatus may determine nextsubsequent combinations of synthetic conditions based on theredetermined priority. The optimizing apparatus may iteratively performthe operation of redetermining the priority and the operation ofdetermining next subsequent combinations of synthetic conditions untilan actual yield from a result of an experiment with subsequentcombinations of synthetic conditions satisfies a preset target yield.Hereinafter, determining subsequent combinations of synthetic conditionsby the optimizing apparatus will be described in more detail withreference to FIG. 7 .

FIG. 4 is a diagram illustrating an example of determining combinationsof synthetic conditions according to an example embodiment. Referring toFIG. 4 , a first neural network 420 may receive graph-type descriptorsG₁, G₂, and G₃ 410 and determine combinations of synthetic conditionsfor generating a target product, a second neural network 430 may outputa predicted value ŷ corresponding to the combinations of the syntheticconditions predicted by the first neural network 420, and a third neuralnetwork 440 may predict a tuning element § indicating whether aprediction model 400 is desirably trained. The third neural network 440may be used to train the prediction model 400 and may not be included inan actual inference.

The first neural network 420 may include, for example, 1-1 neuralnetwork (e.g., MPNN M_(θ)(G₁)) 420-1 configured to receive thedescriptor G₁, 1-2 neural network (e.g., MPNN M_(θ)(G₂)) 420-2configured to receive the descriptor G₂, and 1-3 neural network (e.g.,MPNN M_(θ)(G₃)) 420-3 configured to receive the descriptor G₃. However,examples of the first neural network 420 are not limited to theforegoing. The descriptors G₁ and G₂ may be graph-type descriptorsrepresenting 3D structures of reactants for generating a target product,and the descriptor G₃ may correspond to a graph-type descriptorrepresenting a 3D structure of the target product.

The first neural network 420 may extract features or feature vectors ofsynthetic conditions corresponding to each descriptor. The featurevectors of the synthetic conditions corresponding to each descriptorextracted by the first neural network 420 may be applied to the secondneural network 430 and the third neural network 440. The second neuralnetwork 430 and the third neural network 440 may each be afully-connected neural network including a plurality of layers.

The second neural network 430 may output a predicted value ŷcorresponding to the feature vectors of the synthetic conditionscorresponding to each descriptor. For example, the predicted value ŷ mayinclude combinations of synthetic conditions and uncertaintiescorresponding to the combinations. The predicted value ŷ may include,for example, a vector 431 indicating a yield ŷ_(lig) corresponding to aligand, a vector 433 indicating a yield ŷ_(bas) corresponding to a basecondition, and a vector 435 indicating a yield ŷ_(sol) corresponding toa solvent condition. However, the predicted value ŷ is not limited tothe foregoing. The vectors 431, 433, and 435 may represent, as a10-dimensional vector, effective conditions for each of the syntheticconditions such as the ligand condition, the base condition, and thesolvent condition, respectively. A vector corresponding to a darkercolor in the vectors 431, 433, and 435 may correspond to a moreeffective condition, i.e., a synthetic condition exhibiting a higheryield.

For example, the optimizing apparatus may select 20 vectors obtained bycombining more effective conditions from the vectors 431, 433, and 435to be combinations 450 of synthetic conditions. The combinations 450 ofthe synthetic conditions may be sequentially arranged according to anorder starting with a highest yield corresponding to combinations ofsynthetic conditions.

The selecting of the combinations 450 of the synthetic conditions maycorrespond to setting a bias for a range of conditions to be optimizedaccording to a corresponding reaction mechanism. This is because a typeof synthetic condition may differ based on a reaction mechanism, and anelement varying a yield may differ for each reaction mechanism.

In addition, the tuning element § output by the third neural network 440may correspond to a value indicating whether the prediction model 400 isdesirably trained. For example, the tuning element § may have a value of1 when the prediction model 400 is desirably trained, and have a valueof 0 when the prediction model 400 is not trained desirably.

Hereinafter, an example where descriptors corresponding to reactants anda target product are input to the first neural network 420 will bedescribed for convenience of description, but examples are not limitedto the foregoing example.

For example, for predicting the combinations 450 of the syntheticconditions for synthesizing the target product, the descriptors G₁, G₂,and G₃ may be input to the first neural network 420. In this example,the first neural network 420 may concatenate features of the syntheticconditions extracted from the descriptors G₁, G₂, and G₃ and output aresult in the form of a vector. The first neural network 420 may sharethe same parameters. The first neural network 420 may include neuralnetworks trained with a machine learning algorithm that learns data fromscientific papers and the like and finally recommends an optimalcondition for a specific reaction.

In contrast, for predicting combinations of synthetic conditions forsynthetizing the target product and a synthetic path, the descriptor G₃corresponding to the target product without the other descriptorscorresponding to the reactants may be input to the first neural network420. In this case, the second neural network 430 may output thecombinations of the synthetic conditions corresponding to the reactantsand synthetic paths for synthesizing the target product. Also, thesecond neural network 430 may classify the descriptors G₁ and G₂ of thereactants by the vectors 431, 433, and 435. In addition, the secondneural network 430 may further output an uncertainty corresponding to apredicted value.

In an example, synthetic conditions for obtaining a high yield in anexperiment for synthesizing a target product may be more effectivelyoptimized based on machine learning, and a strategy for suchoptimization may be more flexibly changed by outputting both a predictedvalue and an uncertainty (or confidence) of the prediction model.

FIG. 5 is a diagram illustrating an example of selecting an initialcondition combination according to an example embodiment. Referring toFIG. 5 , an initial condition combination may be selected based on aconfidence (e.g., a first confidence) corresponding to a yieldcorresponding to combinations of synthetic conditions obtained asdescribed above with reference to FIG. 4 , and an experiment may then beperformed based thereon.

As described above with reference to FIG. 4 , the optimizing apparatusmay select an initial condition combination from among the combinations450 of the synthetic conditions, which are more effective conditionsconcatenated from the vector 431, the vector 433, and the vector 435.

The optimizing apparatus may predict 20 different vectors through arandom dropout of connections of lines (e.g., edges) connecting layersin the second neural network 430 including the layers. That is, forexample, the optimizing apparatus may connect some and disconnect someof layers outputting the vector 431, the vector 433, and the vector 435,to diversify the connections, and may thereby predict the 20 vectorscorresponding to the initial condition combination among thecombinations 450 of the synthetic conditions.

In this example, when a deviation among the 20 vectors is greater than apreset threshold, a first confidence (or uncertainty) corresponding to apredicted result of a prediction model may be determined to be low. Incontrast, when the deviation among the 20 vectors is less than thepreset threshold, the first confidence (or uncertainty) corresponding tothe predicted result of the prediction model may be determined to behigh.

In operation 510, the optimizing apparatus may determine a selectionratio of the initial condition combination among the combinations 450 ofthe synthetic conditions based on a confidence (or a first confidence)corresponding to a yield of the combinations 450 of the syntheticconditions. When selecting the initial condition combination, theoptimizing apparatus may determine the selection ratio including a firstratio (exploitation) by which the initial condition combination isselected based on a predicted value of the prediction model and a secondratio (exploration) by which the initial condition combination isselected to include various conditions.

For example, when the first confidence is greater than a presetthreshold, the optimizing apparatus may increase the first ratio anddecrease the second ratio. In contrast, when the first confidence isless than the preset threshold due to, for example, a lack of data and anew synthetic type, the optimizing apparatus may increase the secondratio and decrease the first ratio.

The optimizing apparatus may provide a predicted value of the predictionmodel along with a corresponding confidence (or a first confidence) anddetermine the selection ratio of the initial condition combination basedon the first confidence. When the first confidence is greater than thepreset threshold because the initial condition combination correspondsto simple known synthetic conditions, the optimizing apparatus mayincrease the first ratio, compared to the second ratio, in the selectionratio of the initial condition combination to find a combination ofsynthetic conditions in early stages. In contrast, when the firstconfidence is less than the preset threshold due to, for example, a lackof data and a new synthetic type, the optimizing apparatus may increasethe second ratio, compared to the first ratio, and adopt variousconditions to synthesize a target product under sufficiently diversifiedconditions. The optimizing apparatus may select the initial conditioncombination based on the selection ratio.

In operation 520, the optimizing apparatus may perform an experimentbased on the selection ratio of the initial condition combinationdetermined in operation 510.

In operation 530, the optimizing apparatus may find an optimalcombination of synthetic conditions with a high yield based on adifference between a predicted yield (indicated as GNN) corresponding toa predicted result of the prediction model and an actual yield(indicated as BO) corresponding to an optimization method formatted to aresult of a current experiment by Bayesian optimization. The term“actual yield” may be also referred to a ground-truth yield. Theoptimizing apparatus may optimize combinations of synthetic conditionsby assigning different weights to a combination of synthetic conditionscorresponding to the predicted yield and a combination of syntheticconditions corresponding to the actual yield based on the differencebetween the predicted yield and the actual yield and the confidence ofthe prediction model, and adjusting a priority between the combinationsof the synthetic conditions.

FIG. 6 is a flowchart illustrating an example of determining a priorityof combinations according to an example embodiment. Operations to bedescribed hereinafter may be performed in sequential order, but not benecessarily performed in sequential order. For example, the order of theoperations may change, and at least two of the operations may beperformed in parallel.

The optimizing apparatus may determine a priority of combinations ofsynthetic conditions for a subsequent experiment based on a result ofcomparing a first yield corresponding to a combination of syntheticconditions predicted by a prediction model before being updated and asecond yield corresponding to a combination of synthetic conditionspredicted by a prediction model after being updated in operation 340described above with reference to FIG. 3 .

Referring to FIG. 6 , the optimizing apparatus may determine a priorityof combinations of synthetic conditions by performing operations 610through 630 to be described hereinafter.

In operation 610, the optimizing apparatus may determine whether thesecond yield is less than or equal to the first yield. In operation 620,when the second yield is determined to be less than or equal to thefirst yield in operation 610, the optimizing apparatus may determine afirst combination corresponding to the first yield to have a higherpriority and may determine a second combination corresponding to thesecond yield to have a lower priority.

In operation 630, when the second yield is determined to be greater thanthe first yield in operation 610, the optimizing apparatus may determinethe second combination to have the higher priority and the firstcombination to have the lower priority.

The optimizing apparatus may perform a subsequent experiment based onthe determined priority.

FIG. 7 is a flowchart illustrating an example of determining subsequentcombinations of synthetic conditions according to an example embodiment.Operations to be described hereinafter may be performed in sequentialorder, but not be necessarily performed in sequential order. Forexample, the order of the operations may change, and at least two of theoperations may be performed in parallel.

Referring to FIG. 7 , the optimizing apparatus may determine subsequentcombinations of synthetic conditions by performing operations 710through 750 to be described hereinafter.

In operation 710, the optimizing apparatus may determine subsequentcombinations of synthetic conditions based on a priority determined asdescribed above with reference to FIG. 6 . A subsequent combination(s)of synthetic conditions described herein may refer to a combination(s)of synthetic conditions for a subsequent experiment.

In operation 720, the optimizing apparatus may provide feedback on anactual yield measured from a result of an experiment performed with thesubsequent combinations of the synthetic conditions determined inoperation 710.

In operation 730, the optimizing apparatus may compare the actual yieldfed back in operation 720 and a preset target yield. The optimizingapparatus may determine whether the actual yield is greater than thetarget yield. For example, when the actual yield is determined to begreater than the target yield in operation 730, the optimizing apparatusmay end the operations.

In operation 740, when the actual yield is determined to be less than orequal to the target yield in operation 730, the optimizing apparatus mayupdate a prediction model based on the actual yield.

In operation 750, the optimizing apparatus may redetermine the priorityof the subsequent combinations of the synthetic conditions through theprediction model updated in operation 740. The optimizing apparatus maythen perform operation 710 based on the redetermined priority.

FIG. 8 is a flowchart illustrating another example of a method ofoptimizing synthetic conditions according to an example embodiment.Operations to be described hereinafter may be performed in sequentialorder, but not be necessarily performed in sequential order. Forexample, the order of the operations may change, and at least two of theoperations may be performed in parallel.

Referring to FIG. 8 , the optimizing apparatus may optimize syntheticconditions by performing operations 810 through 860 to be describedhereinafter.

In operation 810, the optimizing apparatus may receive a graph-typedescriptor. For example, the optimizing apparatus may receive, through aUI, the graph-type descriptor including 3D structural information of areactant and 3D structural information of a target product to besynthesized by the reactant. Alternatively, the optimizing apparatus mayalso receive a reaction mechanism for generating the target productthrough the reactant.

In operation 820, the optimizing apparatus may determine combinations ofsynthetic conditions for generating the target product by applying thedescriptor including a graph-type molecular structure to a neuralnetwork-based prediction model.

In operation 830, the optimizing apparatus may output yieldscorresponding to the combinations of the synthetic conditions determinedby the prediction model in operation 820 and/or a confidencecorresponding to the yields.

In operation 840, the optimizing apparatus may select an initialcondition and/or an initial condition combination for an experiment. Theoptimizing apparatus may select the initial condition combination basedon the confidence (e.g., a first confidence) corresponding to the yieldsof the combinations of the synthetic conditions.

For example, when an initial experiment is performed ten times, theoptimizing apparatus may preferentially select a combination ofsynthetic conditions predicted by the prediction model for the ten timesof the experiment. In this example, when the first confidence is low,the optimizing apparatus may preferentially select another combinationof synthetic conditions relatively different from the combinationpredicted by the prediction model.

For example, under the assumption that the first confidence is 90% whichis 75% higher than a preset threshold, the optimizing apparatus mayselect a combination of synthetic conditions predicted by the predictionmodel for nine out of the ten times corresponding to 90% (or a firstratio), and select a combination of random conditions or a combinationof synthetic conditions different from the combination predicted by theprediction model for the remaining one time out of the ten timescorresponding to 10% (or a second ratio).

For another example, under the assumption that a confidence of a resultpredicted by the prediction model is 20% which is 75% lower than thepreset threshold, the optimizing apparatus may select a combination ofsynthetic conditions predicted by the prediction model as the initialcondition combination for two times out of the ten times whichcorresponds to 20%, and select another combination of syntheticconditions that is not selected by the prediction model as the initialcondition combination for eight times out of the ten times whichcorresponds to 80%.

In operation 850, the optimizing apparatus may perform the experimentwith the initial condition combination determined in operation 840. Forexample, the optimizing apparatus may perform the experiment with theinitial condition combination determined in operation 840 by a robot,and output an actual yield measured from a result of the experimentperformed with the initial condition combination.

In operation 860, the optimizing apparatus may select an optimalcombination of synthetic conditions based on a confidence (or a secondconfidence) of the prediction model, an error of the prediction model,the time of iterations, and the like. For example, after verifying anactual yield measured from a result of initial ten times of theexperiment, the optimizing apparatus may determine a priority among thecombinations of the synthetic conditions based on a difference betweenthe actual yield and a predicted yield of the prediction model. In thisexample, the optimizing apparatus may assign different weights tocombinations of synthetic conditions respectively corresponding to theactual yield and the predicted yield based on the second confidence andadjust a priority of the combinations of the synthetic conditions, andmay thereby optimize the combinations of the synthetic conditions toobtain a high yield.

In operation 860, the optimizing apparatus may determine a priority ofcombinations of synthetic conditions for a subsequent time of theexperiment through a comprehensive determination in which the predictionmodel trained through data and a yield inference method dependent on anactual experiment result such as a Bayesian optimization method arecombined.

When obtaining an intended target yield through the foregoingoperations, the optimizing apparatus may update the prediction model bystoring the combinations of the synthetic conditions corresponding tothe priority determined in operation 860 in a database used for trainingthe prediction model, and use the database for further similarreactions.

FIG. 9 is a flowchart illustrating still another example of a method ofoptimizing synthetic conditions according to an example embodiment.Operations to be described hereinafter may be performed in sequentialorder, but not be necessarily performed in sequential order. Forexample, the order of the operations may change, and at least two of theoperations may be performed in parallel.

Referring to FIG. 9 , the optimizing apparatus may optimize syntheticconditions by redetermining a search space by performing operations 910through 960 to be described hereinafter.

In operation 910, the optimizing apparatus may receive a graph-typedescriptor including structural information of at least one reactant,structural information of a target product to be synthesized by thereactant, and a synthetic path of the reactant, and receive a reactionmechanism of the reactant.

In operation 920, the optimizing apparatus may determine a search spaceincluding combinations of synthetic conditions for generating the targetproduct and a synthetic path, which satisfy the reaction mechanism, byapplying the descriptor to a neural network-based prediction model. Theprediction model may include a neural network trained to predict atleast one among combinations of synthetic conditions, predicted yieldscorresponding to the combinations of the synthetic conditions, a firstconfidence corresponding to the predicted yields, and a synthetic path,which satisfy the received reaction mechanism of the reactant.

In operation 930, the optimizing apparatus may sample at least oneinitial condition combination among the combinations based on a firstconfidence corresponding to a yield of the combinations in the searchspace determined in operation 920. The optimizing apparatus maydetermine a selectin ratio including a first ratio by which syntheticconditions predicted by the prediction model are selected to be theinitial condition combination and a second ratio by which randomconditions are selected to be the initial condition combination, andsample the initial condition combination based on the determinedselection ratio. When the first confidence is greater than a presetthreshold, the optimizing apparatus may increase the first ratio anddecrease the second ratio. In contrast, when the first confidence isless than the preset threshold, the optimizing apparatus may increasethe second ratio and decrease the first ratio.

In operation 940, the optimizing apparatus may update the predictionmodel based on an actual yield measured from a result of an experimentwith the initial condition combination sampled in operation 930. Theoptimizing apparatus may update the prediction model based on a resultof comparing a predicted yield corresponding to a combination ofsynthetic conditions determined by the prediction model and the actualyield from the result of the experiment with the initial conditioncombination.

In operation 950, the optimizing apparatus may determine a priorityamong the combinations of the synthetic conditions based on theprediction model updated in operation 940. The optimizing apparatus maycompare a first yield corresponding to a combination of syntheticconditions predicted by the prediction model yet to be updated and asecond yield corresponding to a combination of synthetic conditionspredicted by the updated prediction model. For example, when the secondyield is less than or equal to the first yield, the optimizing apparatusmay determine a first combination corresponding to the first yield tohave a higher priority and a second combination corresponding to thesecond yield to have a lower priority. In contrast, when the secondyield is greater than the first yield, the optimizing apparatus maydetermine the second combination to have the higher priority and thefirst combination to have the lower priority.

In operation 960, the optimizing apparatus may redetermine the searchspace such that it includes subsequent combinations of syntheticconditions and a subsequent synthetic path based on the prioritydetermined in operation 950. Here, as the prediction model is updatedbased on a result of an experiment with the subsequent combinations ofthe synthetic conditions, the priority may be redetermined, and thesearch space may be determined such that it includes next subsequentcombinations of synthetic conditions based on the redetermined priority.

FIG. 10 is a diagram illustrating another example of a method ofoptimizing synthetic conditions according to an example embodiment.Referring to FIG. 10 , the optimizing apparatus may redetermine a searchspace.

When at least one reactant is given, the optimizing apparatus mayautomatically update an acquisition function (e.g., f_(HDO)) forcalculating a priority among combinations of synthetic conditions for asubsequent experiment while iteratively performing the followingoperations.

In operation 1010, the optimizing apparatus may receive a graph-typedescriptor (e.g., G(r₁, r₂, p)) including structural information of atleast one reactant, structural information of a target product to besynthesized by the reactant, and a synthetic path of the reactant.

In operation 1020, the optimizing apparatus may determine a search space1025 including combinations of synthetic conditions for generating thetarget product and a synthetic path, which satisfy a reaction mechanism,by applying the descriptor G(r₁, r₂, p) to a neural network-basedprediction model. For example, the optimizing apparatus may define thesearch space 1025 based on a reaction including, as a graph-typemolecular structure, a structure of the reactant and a structure of thetarget product. The search space 1025 may be a space to which apredicted result of a GNN (e.g., f_(GNNs)) and an uncertaintycorresponding to the predicted result are applied. For example, theoptimizing apparatus may narrow the search space 1025 using the GNNmodel (e.g., f_(GNNs)) that predicts a chemical context (including, forexample, a catalyst, a base, and a ligand) most suitable for a specificorganic reaction in which the structure of the reactant and/or targetproduct is given.

In operation 1030, the optimizing apparatus may sample an initialcondition combination. For example, as illustrated in 1035, theoptimizing apparatus may determine, for the initial conditioncombination, a selection ratio for sampling combinations (first andsecond combinations) of synthetic conditions predicted by the GNN by afirst ratio (e.g., 50%), and sampling combinations of syntheticconditions (third and fourth combinations) predicted by maximum Lainhypercube sampling (maximum-LHS) by a second ratio (e.g., 50%).

For example, the optimizing apparatus may include an independentprediction model which is a multi-label classification model trainedwith approximately 10 million examples in a Reaxys database andconfigured to define candidate condition substances by each contextcondition area. The optimizing apparatus may select, from among limitedcandidates (candidate combinations of synthetic conditions), acombination of synthetic conditions from which a target yield isexpected to be derived through an initial experiment.

The optimizing apparatus may determine the selection ratio of thecombinations of the synthetic conditions based on a balance betweenexploitation and exploration in the search space 1025 to sample theinitial condition combination. The optimizing apparatus may adoptcandidates of conditions predicted by the GNN model (e.g., f_(GNNs)) bythe first ratio and adopt combinations of synthetic conditions predictedusing maximum-LHS by the second ratio, through sampling.

In operation 1040, the optimizing apparatus may perform an experiment(or the initial experiment) with the initial condition combinationsampled in operation 1030 and output an actual yield measured from aresult of the experiment as illustrated in a graph 1045. The actualyield may correspond to, for example, four initial conditioncombinations sampled in operation 1030. When performing the experimentwith the initial condition combination sampled in operation 1030, theoptimizing apparatus may learn a reaction result from a conversion yieldmeasured by liquid chromatography-mass spectrometry (LC-MS) to obtain anobjective function for a model f_(BO_UCB) to which an uncertainty isapplied based on Bayesian optimization for an actual yield. It may bedesirable to find an optimal combination of conditions through theinitial experiment for obtaining a desired target yield. However, inmany cases, it may not be easy to find the optimal combination throughthe initial condition combination.

In operation 1050, as illustrated in a graph 1055, the optimizingapparatus may optimize synthetic conditions by updating the predictionmodel based on the actual yield. In this case, the optimizing apparatusmay optimize the synthetic conditions based on the predicted result ofthe GNN (f_(GNNs)) and the predicted result to which the uncertaintyF_(BO-UCB) based on Bayesian optimization for the actual yield isapplied.

In operation 1060, the optimizing apparatus may determine a priorityamong the combinations of the synthetic conditions based on theprediction model updated in operation 1050. The optimizing apparatus maycalculate a priority of subsequent combinations of synthetic conditionsin the form of ensemble based on a result history, the GNN model (e.g.,f_(GNNs)), and an optimization model (GP). The optimizing apparatus maydetermine the priority through a comprehensive determination using anexperiment result, a frequency of previous experiments, and anuncertainty of an objective function of the prediction model, inaddition to the predicted result of the GNN model (e.g., f_(GNNs)), tomaximize efficiency.

The optimizing apparatus may determine the priority f_(HDO) based onEquation 2, for example.

f _(UHDO) =N{(f _(GNNs))·/log(t)}·a+N{(f _(BO_UCB))}·b  [Equation 2]

In Equation 2, t denotes the number of iterations, and N denotes a valueof normalization for preventing a value on one side from absorbinginformation on another side because the value on one side is extremelystood out. In addition, a and b denote weights.

The optimizing apparatus may determine the priority among thecombinations of the synthetic conditions by assigning a highest priorityto a combination of synthetic conditions corresponding to a highestyield, as illustrated in 1065.

In operation 1070, the optimizing apparatus may redetermine the searchspace as illustrated in a graph 1075 such that it includes subsequentcombinations of synthetic conditions and a subsequent synthetic pathbased on the priority determined in operation 1060, and then perform anexperiment on the redetermined search space in operation 1040.

The optimizing apparatus may iteratively perform the foregoingoperations until it finds an optimal combination of synthetic conditionsfrom which the target yield is obtained, and update the prediction modeleach time an experiment is completed.

The foregoing operations may be applied to a platform in which anorganic synthesis experiment is performed in a fully automated mannerusing, for example, a robot and management software.

FIG. 11 is a diagram illustrating an example of an optimizing apparatusaccording to an example embodiment. Referring to FIG. 11 , an optimizingapparatus 1100 may include a UI 1110, a memory 1150, and a processor1130. The UI 1100, the memory 1150, and the processor 1130 may beconnected to one another through a communication bus 1105.

The UI 1110 may receive a graph-type descriptor including structuralinformation of at least one reactant and structural information of atarget product to be synthesized by the reactant. Also, the UI 1110 maybe an input means for receiving a result of an experiment as feedback.The UI 1110 may include, for example, a keypad, a dome switch, atouchpad, a jog wheel, a jog switch, and the like, but is not limitedthereto. The touchpad may include, for example, a contact capacitivetype, a pressure resistive type, an infrared sensing type, a surfaceultrasonic conduction type, an integral tension measurement type, apiezoelectric effect type, and the like, but is not limited thereto.

Alternatively, according to another example embodiment, the UI 1110 mayreceive a graph-type descriptor including structural information of atleast one reactant, structural information of a target product to besynthesized by the reactant, and a synthetic path including at least oneof substituents and reactors of the reactant, and receive a reactionmechanism of the reactant.

The processor 1130 may operate a neural network by executing at leastone program stored in the memory 1150. The processor 1130 may determinecombinations of synthetic conditions for generating the target productby applying the descriptor to a prediction model that is based on theneural network. The processor 1130 may select at least one initialcondition combination from among the combinations based on a firstconfidence corresponding to a yield of the combinations of the syntheticconditions. The processor 1130 may update the prediction model based onan actual yield measured from a result of an experiment with the initialcondition combination. The processor 1130 may determine a priority amongthe combinations based on the updated prediction model. The processor1130 may determine subsequent combinations of synthetic conditions basedon the determined priority. The processor 1130 may redetermine thepriority as the prediction model is updated based on an actual yieldmeasured from a result of an experiment with the subsequent combinationsof the synthetic conditions. The processor 1130 may determine nextsubsequent combinations of synthetic conditions that follow thesubsequent combinations based on the redetermined priority, anditeratively perform the redetermining of the priority and thedetermining of the next subsequent combinations until an actual yieldmeasured from a result of an experiment with the next subsequentcombinations satisfies a preset target yield.

The processor 1130 may update the prediction model by receiving anexperiment result as feedback through the UI 1110.

The processor 1130 may execute executable instructions included in thememory 1150. When the instructions are executed in the processor 1130,the processor 1130 may invoke the neural network-based prediction modelfrom the memory 1150 and apply the descriptor to the prediction model.The processor 1130 may execute a program and control the optimizingapparatus 1100. A code of the program executed by the processor 1130 maybe stored in the memory 1150.

Alternatively, according to another example embodiment, the processor1130 may determine a search space including combinations of syntheticconditions for generating a target product and a synthetic path, whichsatisfy a reaction mechanism, by applying a descriptor to a neuralnetwork-based prediction model. The processor 1130 may sample at leastone initial condition combination among the combinations of thesynthetic conditions based on a first confidence corresponding to ayield of the combinations in the search space. The processor 1130 mayupdate the prediction model based on an actual yield measured from aresult of an experiment with the initial condition combination. Theprocessor 1130 may determine a priority among the combinations based onthe updated prediction model. The processor 1130 may redetermine thesearch space such that it includes subsequent combinations of syntheticconditions based on the determined priority. In this case, as theprediction model is updated based on an actual yield measured from aresult of an experiment with the subsequent combinations of thesynthetic conditions, the priority may be redetermined accordingly, andthe search space may be determined such that it includes next subsequentcombinations of synthetic conditions based on the redetermined priority.

In addition, the processor 1130 may perform one or more, or all, of themethods or operations described above with reference to FIGS. 1 through10 . The processor 1130 may be a hardware-implemented processing devicehaving a physically structured circuit to execute desired operations.The desired operations may include, for example, codes or instructionsincluded in a program. The optimizing apparatus 1100 implemented byhardware may include, for example, a microprocessor, a centralprocessing unit (CPU), a graphics processing unit (GPU), a processorcore, a multi-core processor, a multiprocessor, an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), and aneural processing unit (NPU).

The memory 1150 may store therein at least one program. In addition, thememory 1150 may store therein various sets of information generatedduring processing performed by the processor 1130. Alternatively, thememory 1150 may store therein the neural network-based prediction model.In addition, the memory 1150 may store therein various sets of data andprograms. The memory 1150 may include a volatile memory or a nonvolatilememory. The memory 1150 may include a large-capacity storage medium suchas a hard disk to store various sets of data therein.

The units described herein may be implemented using hardware componentsand software components. For example, the hardware components mayinclude microphones, amplifiers, band-pass filters, audio to digitalconvertors, non-transitory computer memory and processing devices. Aprocessing device may be implemented using one or more general-purposeor special purpose computers, such as, for example, a processor, acontroller and an arithmetic logic unit (ALU), a digital signalprocessor, a microcomputer, a field programmable gate array (FPGA), aprogrammable logic unit (PLU), a microprocessor or any other devicecapable of responding to and executing instructions in a defined manner.The processing device may run an operating system (OS) and one or moresoftware applications that run on the OS. The processing device also mayaccess, store, manipulate, process, and create data in response toexecution of the software. For purpose of simplicity, the description ofa processing device is used as singular; however, one skilled in the artwill appreciated that a processing device may include multipleprocessing elements and multiple types of processing elements. Forexample, a processing device may include multiple processors or aprocessor and a controller. In addition, different processingconfigurations are possible, such as parallel processors.

The software may include a computer program, a piece of code, aninstruction, or some combination thereof, to independently orcollectively instruct or configure the processing device to operate asdesired. Software and data may be embodied permanently or temporarily inany type of machine, component, physical or virtual equipment, computerstorage medium or device, or in a propagated signal wave capable ofproviding instructions or data to or being interpreted by the processingdevice. The software also may be distributed over network coupledcomputer systems so that the software is stored and executed in adistributed fashion. The software and data may be stored by one or morenon-transitory computer readable recording mediums. The non-transitorycomputer readable recording medium may include any data storage devicethat can store data which can be thereafter read by a computer system orprocessing device.

Example embodiments include non-transitory computer-readable mediaincluding program instructions to implement various operations embodiedby a computer. The media may also include, alone or in combination withthe program instructions, data files, data structures, tables, and thelike. The media and program instructions may be those specially designedand constructed for the purposes of example embodiments, or they may beof the kind well known and available to those having skill in thecomputer software arts. Examples of non-transitory computer-readablemedia include magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD ROM disks; magneto-optical mediasuch as floptical disks; and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM) and random-access memory (RAM). Examples of programinstructions include both machine code, such as produced by a compiler,and files containing higher level code that may be executed by thecomputer using an interpreter.

The described hardware devices may be configured to act as one or moresoftware modules in order to perform the operations of theabove-described example embodiments, or vice versa.

The foregoing exemplary embodiments are merely exemplary and are not tobe construed as limiting. The present teaching can be readily applied toother types of apparatuses. Also, the description of the exemplaryembodiments is intended to be illustrative, and not to limit the scopeof the claims, and many alternatives, modifications, and variations willbe apparent to those skilled in the art.

What is claimed is:
 1. A method of optimizing synthetic conditions, themethod comprising: receiving a graph-type descriptor comprising at leastone of structural information of at least one reactant and structuralinformation of a target product to be synthesized by the reactant;determining combinations of synthetic conditions for generating thetarget product by applying the graph-type descriptor to a predictionneural network model; selecting at least one initial conditioncombination from among the combinations based on a first confidencecorresponding to a yield of the combinations; updating the predictionneural network model based on a ground-truth yield obtained from aresult of an experiment with the initial condition combination;determining a priority of the combinations based on the updatedprediction neural network model; and determining subsequent combinationsof synthetic conditions based on the determined priority.
 2. The methodof claim 1, wherein the selecting of the initial condition combinationcomprises: determining a selection ratio for the initial conditioncombination based on the first confidence; and selecting the initialcondition combination based on the selection ratio.
 3. The method ofclaim 2, wherein the determining of the selection ratio comprises:determining the selection ratio comprising a first ratio by whichsynthetic conditions predicted by the prediction neural network modelare selected to be the initial condition combination and a second ratioby which random conditions are selected to be the initial conditioncombination, based on the first confidence.
 4. The method of claim 3,wherein the determining of the selection ratio comprises: when the firstconfidence is greater than a preset threshold, increasing the firstratio and decreasing the second ratio; and when the first confidence isless than the preset threshold, increasing the second ratio anddecreasing the first ratio.
 5. The method of claim 1, wherein theprediction neural network model is trained to predict at least one ofthe combinations, predicted yields corresponding to the combinations,and the first confidence corresponding to the predicted yields.
 6. Themethod of claim 1, wherein the prediction neural network model uses amessage passing neural network (MPNN) configured to determine thecombinations according to a correlation between neighboring atoms in thereactant based on the graph-type descriptor.
 7. The method of claim 1,wherein the updating of the prediction neural network model comprises:updating the prediction neural network model based on a result ofcomparing a predicted yield corresponding to a combination of syntheticconditions determined by the prediction neural network model and theground-truth yield.
 8. The method of claim 1, wherein the determining ofthe priority comprises: comparing a first yield corresponding to acombination of synthetic conditions predicted by the prediction neuralnetwork model yet to be updated and a second yield corresponding to acombination of synthetic conditions predicted by the updated predictionneural network model; and determining a priority between thecombinations based on a result of the comparing.
 9. The method of claim8, wherein the determining of the priority between the combinationsbased on the result of the comparing comprises: when the second yield isless than or equal to the first yield, determining a first combinationcorresponding to the first yield to have a higher priority anddetermining a second combination corresponding to the second yield tohave a lower priority; and when the second yield is greater than thefirst yield, determining the second combination to have the higherpriority and the first combination to have the lower priority.
 10. Themethod of claim 1, wherein the determining of the subsequentcombinations of the synthetic conditions comprises: redetermining thepriority as the prediction neural network model is updated based on aground-truth yield obtained from a result of an experiment with thesubsequent combinations of the synthetic conditions; determining nextsubsequent combinations of synthetic conditions that follow thesubsequent combinations based on the redetermined priority; anditeratively performing the redetermining of the priority and thedetermining of the next subsequent combinations until a ground-truthyield obtained from a result of an experiment with the next subsequentcombinations satisfies a preset target yield.
 11. The method of claim 1,wherein the synthetic conditions comprise at least one of a catalystcondition, a ligand condition, a base condition, a solvent condition, atemperature condition, a density condition, a humidity condition, areaction time condition, and a pressure condition.
 12. The method ofclaim 1, wherein the graph-type descriptor further comprises a syntheticpath comprising at least one of substituents and reactors of thereactant.
 13. The method of claim 12, wherein the determining of thecombinations of the synthetic conditions comprises: determining thecombinations of the synthetic conditions to satisfy a reaction mechanismof the reactant by applying the graph-type descriptor to the predictionneural network model.
 14. A method of optimizing synthetic conditions,comprising: receiving a graph-type descriptor comprising structuralinformation of at least one reactant and structural information of atarget product to be synthesized by the reactant, and a synthetic pathof the reactant, and receiving a reaction mechanism of the reactant;determining a search space comprising combinations of syntheticconditions for generating the target product and the synthetic path thatsatisfy the reaction mechanism by applying the graph-type descriptor toa prediction neural network model; sampling at least one initialcondition combination among the combinations based on a first confidencecorresponding to a yield of the combinations in the search space;updating the prediction neural network model based on a ground-truthyield obtained from a result of an experiment with the initial conditioncombination; determining a priority of the combinations based on theupdated prediction neural network model; and redetermining the searchspace to include subsequent combinations of synthetic conditions and asubsequent synthetic path based on the determined priority, wherein, asthe prediction neural network model is updated based on a result of anexperiment with the subsequent combinations, the priority isredetermined, and the search space is determined to include nextsubsequent combinations of synthetic conditions that follow thesubsequent combinations based on the redetermined priority.
 15. Themethod of claim 14, wherein the sampling of the initial conditioncombination comprises: determining a selection ratio comprising a firstratio by which synthetic conditions predicted by the prediction neuralnetwork model are selected to be the initial condition combination and asecond ratio by which random conditions are selected to be the initialcondition combination, based on the first confidence.
 16. The method ofclaim 15, wherein the determining of the selection ratio comprises: whenthe first confidence is greater than a preset threshold, increasing thefirst ratio and decreasing the second ratio; and when the firstconfidence is less than the preset threshold, increasing the secondratio and decreasing the first ratio.
 17. The method of claim 14,wherein the prediction neural network model is trained to predict atleast one of the combinations, predicted yields corresponding to thecombinations, the first confidence corresponding to the predictedyields, and the synthetic path, which satisfy the reaction mechanism.18. The method of claim 14, wherein the updating of the prediction modelcomprises: updating the prediction neural network model based on aresult of comparing a predicted yield corresponding to a combination ofsynthetic conditions determined by the prediction neural network modeland the ground-truth yield obtained from the result of the experimentwith the initial condition combination.
 19. The method of claim 14,wherein the determining of the priority comprises: comparing a firstyield corresponding to a combination of synthetic conditions predictedby the prediction neural network model yet to be updated and a secondyield corresponding to a combination of synthetic conditions predictedby the updated prediction neural network model; when the second yield isless than or equal to the first yield, determining a first combinationcorresponding to the first yield to have a higher priority anddetermining a second combination corresponding to the second yield tohave a lower priority; and when the second yield is greater than thefirst yield, determining the second combination to have the higherpriority and the first combination to have the lower priority.
 20. Anon-transitory computer-readable storage medium storing instructionsthat are executable by a processor to perform the method of claim
 1. 21.An apparatus for optimizing synthetic conditions, the apparatuscomprising: a user interface (UI) configured to receive a graph-typedescriptor comprising at least one of structural information of at leastone reactant and structural information of a target product to besynthesized by the at least one reactant; a memory storing one or moreinstructions; and a processor configured to execute the one or moreinstructions to: determine combinations of synthetic conditions forgenerating the target product by applying the graph-type descriptor to aprediction neural network model, select at least one initial conditioncombination from among the combinations based on a first confidencecorresponding to a yield of the combinations, update the predictionneural network model based on a ground-truth yield obtained from aresult of an experiment with the initial condition combination,determine a priority of the combinations based on the updated predictionneural network model, and determine subsequent combinations of syntheticconditions based on the determined priority, wherein, as the predictionneural network model is updated based on a result of an experiment withthe subsequent combinations, the priority is redetermined, and nextsubsequent combinations of synthetic conditions are determined based onthe redetermined priority.
 22. The apparatus of claim 21, wherein theprocessor is configured to: determine a selection ratio for the initialcondition combination based on the first confidence, and select theinitial condition combination based on the determined selection ratio.23. The apparatus of claim 22, wherein the processor is configured to:determine the selection ratio comprising a first ratio by whichsynthetic conditions predicted by the prediction neural network modelare selected to be the initial condition combination and a second ratioby which random conditions are selected to be the initial conditioncombination, based on the first confidence.
 24. The apparatus of claim23, wherein the processor is configured to: when the first confidence isgreater than a preset threshold, increase the first ratio and decreasethe second ratio; and when the first confidence is less than the presetthreshold, increase the second ratio and decrease the first ratio. 25.The apparatus of claim 21, wherein the prediction neural network modelis trained to predict at least one of the combinations, predicted yieldscorresponding to the combinations, and the first confidencecorresponding to the predicted yields.
 26. The apparatus of claim 21,wherein the prediction neural network model uses a message passingneural network (MPNN) configured to determine the combinations accordingto a correlation between neighboring atoms in the reactant based on thegraph-type descriptor.
 27. The apparatus of claim 21, wherein theprocessor is configured to: update the prediction neural network modelbased on a result of comparing a predicted yield of a combination ofsynthetic conditions determined by the prediction neural network modeland the ground-truth yield.
 28. The apparatus of claim 21, wherein theprocessor is configured to: compare a first yield corresponding to acombination of synthetic conditions predicted by the prediction neuralnetwork model yet to be updated and a second yield corresponding to acombination of synthetic conditions predicted by the updated predictionneural network model; and determine a priority between the combinationsbased on a result of the comparing.
 29. The apparatus of claim 28,wherein the processor is configured to: when the second yield is lessthan or equal to the first yield, determine a first combinationcorresponding to the first yield to have a higher priority and determinea second combination corresponding to the second yield to have a lowerpriority; and when the second yield is greater than the first yield,determine the second combination to have the higher priority and thefirst combination to have the lower priority.
 30. The apparatus of claim21, wherein the processor is configured to: redetermine the priority asthe prediction neural network model is updated based on a ground-truthyield obtained from a result of an experiment with the subsequentcombinations of the synthetic conditions; determine next subsequentcombinations of synthetic conditions based on the redetermined priority;and iteratively perform the redetermining of the priority and thedetermining of the next subsequent combinations until anotherground-truth yield obtained from a result of an experiment with the nextsubsequent combinations satisfies a preset target yield.
 31. Theapparatus of claim 21, wherein the synthetic conditions comprise atleast one of a catalyst condition, a ligand condition, a base condition,a solvent condition, a temperature condition, a density condition, ahumidity condition, a reaction time condition, and a pressure condition.32. The apparatus of claim 21, wherein the graph-type descriptor furthercomprises a reaction mechanism of the reactant.
 33. The apparatus ofclaim 21, wherein the processor is further configured to: determine thecombinations of the synthetic conditions to satisfy a reaction mechanismof the reactant by applying the graph-type descriptor to the predictionneural network model.
 34. An apparatus for optimizing syntheticconditions, the apparatus comprising: a user interface (UI) configuredto receive a graph-type descriptor comprising structural information ofat least one reactant, structural information of a target product to besynthesized by the at least one reactant, and a synthetic path comparingat least one of substituents and reactors of the at least one reactant,and receive a reaction mechanism of the at least one reactant; a memorystoring one or more instructions; and a processor configured to executethe one or more instructions to: determine a search space comprisingcombinations of synthetic conditions for generating the target productand a synthetic path, which satisfy the reaction mechanism, by applyingthe graph-type descriptor to a prediction neural network model; sampleat least one initial condition combination among the combinations basedon a first confidence corresponding to a yield of the combinations inthe search space; update the prediction neural network model based on aground-truth yield obtained from a result of an experiment with theinitial condition combination; determine a priority of the combinationsbased on the updated prediction neural network model; and redeterminethe search space to include subsequent combinations of syntheticconditions based on the determined priority, wherein, as the predictionneural network model is updated based on a ground-truth yield obtainedfrom a result of an experiment with the subsequent combinations of thesynthetic conditions, the priority is redetermined, and the search spaceis determined to include next subsequent combinations of syntheticconditions based on the redetermined priority.
 35. The apparatus ofclaim 34, wherein the prediction neural network model is trained topredict at least one of the combinations, predicted yields correspondingto the combinations, the first confidence corresponding to the predictedyields, and the synthetic path, which satisfy the reaction mechanism.